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  <h1>Source code for rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>

<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Tuple</span>

<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">Transition</span>
<span class="kn">from</span> <span class="nn">rl_coach.memories.episodic.episodic_hindsight_experience_replay</span> <span class="k">import</span> <span class="n">HindsightGoalSelectionMethod</span><span class="p">,</span> \
    <span class="n">EpisodicHindsightExperienceReplay</span><span class="p">,</span> <span class="n">EpisodicHindsightExperienceReplayParameters</span>
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.experience_replay</span> <span class="k">import</span> <span class="n">MemoryGranularity</span>
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">GoalsSpace</span>


<span class="k">class</span> <span class="nc">EpisodicHRLHindsightExperienceReplayParameters</span><span class="p">(</span><span class="n">EpisodicHindsightExperienceReplayParameters</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay:EpisodicHRLHindsightExperienceReplay&#39;</span>


<div class="viewcode-block" id="EpisodicHRLHindsightExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.episodic.EpisodicHRLHindsightExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">EpisodicHRLHindsightExperienceReplay</span><span class="p">(</span><span class="n">EpisodicHindsightExperienceReplay</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Implements HRL Hindsight Experience Replay as described in the following paper:  https://arxiv.org/abs/1805.08180</span>

<span class="sd">    This is the memory you should use if you want a shared hindsight experience replay buffer between multiple workers</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
                 <span class="n">hindsight_transitions_per_regular_transition</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">hindsight_goal_selection_method</span><span class="p">:</span> <span class="n">HindsightGoalSelectionMethod</span><span class="p">,</span>
                 <span class="n">goals_space</span><span class="p">:</span> <span class="n">GoalsSpace</span><span class="p">,</span>
                 <span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        :param max_size: The maximum size of the memory. should be defined in a granularity of Transitions</span>
<span class="sd">        :param hindsight_transitions_per_regular_transition: The number of hindsight artificial transitions to generate</span>
<span class="sd">                                                             for each actual transition</span>
<span class="sd">        :param hindsight_goal_selection_method: The method that will be used for generating the goals for the</span>
<span class="sd">                                                hindsight transitions. Should be one of HindsightGoalSelectionMethod</span>
<span class="sd">        :param goals_space: A GoalsSpace  which defines the properties of the goals</span>
<span class="sd">        :param do_action_hindsight: Replace the action (sub-goal) given to a lower layer, with the actual achieved goal</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">,</span> <span class="n">hindsight_transitions_per_regular_transition</span><span class="p">,</span> <span class="n">hindsight_goal_selection_method</span><span class="p">,</span>
                         <span class="n">goals_space</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">store_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode</span><span class="p">:</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># for a layer producing sub-goals, we will replace in hindsight the action (sub-goal) given to the lower</span>
        <span class="c1"># level with the actual achieved goal. the achieved goal (and observation) seen is assumed to be the same</span>
        <span class="c1"># for all levels - we can use this level&#39;s achieved goal instead of the lower level&#39;s one</span>

        <span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="n">episode</span><span class="o">.</span><span class="n">transitions</span><span class="p">:</span>
            <span class="n">new_achieved_goal</span> <span class="o">=</span> <span class="n">transition</span><span class="o">.</span><span class="n">next_state</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span><span class="o">.</span><span class="n">goal_name</span><span class="p">]</span>
            <span class="n">transition</span><span class="o">.</span><span class="n">action</span> <span class="o">=</span> <span class="n">new_achieved_goal</span>

        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;An episodic HER cannot store a single transition. Only full episodes are to be stored.&quot;</span><span class="p">)</span></div>
</pre></div>

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